TOOL SUPPORT FOR DATA-DRIVEN SERVICE INNOVATION: A SYSTEMATIC LITERATURE REVIEW

Berndt Jesenko and Stefan Thalmann ()
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Berndt Jesenko: CAMPUS 02, University of Applied Sciences, Koerblergasse 126, 8010 Graz, Austria
Stefan Thalmann: ��Karl-Franzens University, Universitätsstraße 15, 8010 Graz, Austria

International Journal of Innovation Management (ijim), 2024, vol. 28, issue 07n08, 1-37

Abstract: Data-driven technologies changed the way how service innovation is conducted in organisations. The literature discusses the potential of Data-Driven Service Innovation (DDSI) processes, but it is not clear yet what tool support for DDSI looks like. This structured literature review examines the tool support for each phase of DDSI processes. We found clear differences between the DDSI phases and different tools for each phase. In the first phase, tools with batch processing capability are employed for methods like text mining and sentiment analysis, helping to capture evolving customer behaviour and trends to increase the speed of innovation rate. In the second phase, immersive technologies, real-time sentiment analysis and stream analytics are used for the validation of service development processes to increase the likelihood of market success. In the third phase, AI tools with the capability to continuously learn from emerging data and Big Data Analytics tools are combined.

Keywords: Big data analytics; data-driven service innovation; artificial intelligence (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1142/S1363919624300046

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